Tal Ashuach
Impact in
- Biophysics top 5%
- Cell Image Analysis Techniques
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- Single-cell and spatial transcriptomics
- Genomics and Chromatin Dynamics
- RNA Research and Splicing
- Gene expression and cancer classification
- RNA and protein synthesis mechanisms
- Gene Regulatory Network Analysis
- Bioinformatics and Genomic Networks
Papers in
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- Genomics and Chromatin Dynamics 6
- Single-cell and spatial transcriptomics 4
- RNA and protein synthesis mechanisms 2
- CRISPR and Genetic Engineering 2
- RNA Research and Splicing 2
- Gene expression and cancer classification 1
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- Cell Image Analysis Techniques 3
- Co-authors
- Nir Yosef (9 shared papers)Anat Kreimer (6 shared papers)Nadav Ahituv (6 shared papers)Chun Ye (2 shared papers)Fumitaka Inoue (5 shared papers)Michael I. Jordan (1 shared paper)Mariano I. Gabitto (1 shared paper)Rohan V. Koodli (1 shared paper)
- Journals
- Nature Communications (2 papers)Genome biology (2 papers)Nature Methods (1 paper)Nature Protocols (1 paper)Cell stem cell (1 paper)
- Partner nations
- United StatesJapanIsrael
In The Last Decade
Tal Ashuach
9 papers receiving 553 citations
Tal Ashuach's Hit Papers
Peers
Comparison fields: 5 of 58
- Biophysics 53
- Molecular Biology 453
- Cancer Research 54
- Immunology 60
- Genetics 65
Countries citing papers authored by Tal Ashuach
This map shows the geographic impact of Tal Ashuach's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Tal Ashuach with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tal Ashuach more than expected).
Fields of papers citing papers by Tal Ashuach
This network shows the impact of papers produced by Tal Ashuach. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Tal Ashuach. The network helps show where Tal Ashuach may publish in the future.
Co-authors
The 25 scholars most cited alongside Tal Ashuach, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | MultiVI: deep generative model for the integration of multimodal data Hit paper breakdown → | 2023 | 139 |
| 2 | 2019 | 131 | |
| 3 | 2020 | 75 | |
| 4 | 2019 | 75 | |
| 5 | 2019 | 54 | |
| 6 | 2022 | 48 | |
| 7 | 2022 | 28 | |
| 8 | 2019 | 4 | |
| 9 | 2024 | 1 | |
| 10 | 2024 | 0 |
About Tal Ashuach
Tal Ashuach is a scholar working on Molecular Biology, Biophysics, Artificial Intelligence, Aerospace Engineering and Electrical and Electronic Engineering, having authored 10 papers that have together received 555 indexed citations. Recurring topics across this work include Genomics and Chromatin Dynamics (6 papers), Single-cell and spatial transcriptomics (4 papers), Cell Image Analysis Techniques (3 papers), RNA and protein synthesis mechanisms (2 papers), CRISPR and Genetic Engineering (2 papers), RNA Research and Splicing (2 papers), Cancer Genomics and Diagnostics (1 paper) and Gene expression and cancer classification (1 paper). The work is most often cited by research in Biophysics (53 citations), Molecular Biology (453 citations), Cancer Research (54 citations), Immunology (60 citations) and Genetics (65 citations). Tal Ashuach has collaborated with scholars based in United States, Japan and Israel. Frequent co-authors include Nir Yosef, Anat Kreimer, Nadav Ahituv, Chun Ye, Fumitaka Inoue, Michael I. Jordan, Mariano I. Gabitto, Rohan V. Koodli, Giuseppe-Antonio Saldi and Meena Subramaniam. Their work appears in journals such as Nature Communications, Genome biology, Nature Methods, Nature Protocols and Cell stem cell.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.